Resonance: A Plague Tale Legacy Trainer 2026 for Windows with God Mode, Unlock All Codex Entries, Unlock All Chapters, Unlock Everything, Change Resonance Points, Resonance Points Editor, profiles, hotkeys, configs, and a clean desktop workflow.
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Resonance: A Plague Tale Legacy Trainer 2026 for Windows with God Mode, Unlock All Codex Entries, Unlock All Chapters, Unlock Everything, Change Resonance Points, Resonance Points Editor, profiles, hotkeys, configs, and a clean desktop workflow.
| import Foundation | |
| import AVFoundation | |
| @available(iOS 10.0, *) | |
| extension AVSpeechSynthesizer { | |
| func speakIPA(_ ipaString: String, voiceIdentifier: String, willSpeak: ((String) -> Void)? = nil) { | |
| //Set the audio session to playback to ignore mute switch on device | |
| do { | |
| try AVAudioSession.sharedInstance().setCategory(AVAudioSession.Category.playback, options: [.interruptSpokenAudioAndMixWithOthers, .duckOthers]) | |
| } catch { |
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A pattern for building personal knowledge bases using LLMs.
This is an idea file, it is designed to be copy pasted to your own LLM Agent (e.g. OpenAI Codex, Claude Code, OpenCode / Pi, or etc.). Its goal is to communicate the high level idea, but your agent will build out the specifics in collaboration with you.
Most people's experience with LLMs and documents looks like RAG: you upload a collection of files, the LLM retrieves relevant chunks at query time, and generates an answer. This works, but the LLM is rediscovering knowledge from scratch on every question. There's no accumulation. Ask a subtle question that requires synthesizing five documents, and the LLM has to find and piece together the relevant fragments every time. Nothing is built up. NotebookLM, ChatGPT file uploads, and most RAG systems work this way.
The session model (Fable) is the orchestrator, not the worker. Fable is the most expensive tier; spend its tokens only on decomposition, judgment calls, synthesis, and talking to the user. Delegate everything else to subagents via the Agent tool, picking the cheapest model that can do the job well:
-
model: "opus"— the default worker tier. Anything requiring real judgment: implementation, debugging, architecture-aware exploration, adversarial review. -
model: "sonnet"— cheap tier for mechanical or low-stakes work: running tests and reporting output, simple greps/lookups with a known target, rote refactors from an exact spec, formatting, screenshot capture, admin chores. If getting it slightly wrong is cheap to catch, use sonnet. -
Exploration/research: never read broadly yourself. Spawn
Exploreagents (model: opus) with tightly scoped questions; consume their synthesized reports, not raw files. Trivial "find the file that defines X" lookups can go to sonnet.
- SillyTavern-Silly-Hug - https://github.com/kainatquaderee/Silly-Hug
- SillyTavern-Cache-Refresh - https://github.com/OneinfinityN7/Cache-Refresh-SillyTavern
- SillyTavern-FormatFixer - https://github.com/DAurielS/ST-FormatFixer
- SillyTaven-Extension-DynamicLore - https://github.com/AugieIsHere/Extension-DynamicLore
- SillyTaven-Extension-DynamicLore - https://github.com/X00LA/Extension-DynamicLore (newer)
- SillyTavern-WebSearch-Selenium - https://github.com/SillyTavern/SillyTavern-WebSearch-Selenium
- SillyTavern-LALib - https://github.com/LenAnderson/SillyTavern-LALib
- SillyTavern-Chub-Search - https://github.com/city-unit/SillyTavern-Chub-Search
- SillyTavern-Extension-ZerxzLib - https://github-com.translate.goog/ZerxZ/SillyTavern-Extension-ZerxzLib?_x_tr_sl=auto&_x_tr_tl=en&_x_tr_hl=de&_x_tr_pto=wapp
